def run_mpi():
print "Rank :", rank, "started"
npix = hp.nside2npix(config.nside_out)
dim, ind_elements = cov_ut.get_dim(config.pol_type)
bolo_list = [None, None, None]
inv_cov_matrix_local = np.zeros((npix, ind_elements), dtype=np.float)
b_matrix_local = np.zeros((npix, dim), dtype=np.float)
hitmap_local = np.zeros(npix, dtype=np.float)
bolo_segment_dict = get_local_bolo_segment_list(rank, size, config.bolo_list, config.segment_list)
time.sleep(0.1*rank)
print "Rank :", rank, ", Bolos and Segments :", bolo_segment_dict
comm.Barrier()
recon_dir = get_recon_dir()
if rank == 0:
make_data_dirs()
for bolo_name in bolo_segment_dict.keys():
print "Rank :", rank, "Bolos class being generated"
bolo_list = initialise_bolo(bolo_name, bolo_list)
for segment in bolo_segment_dict[bolo_name]:
prompter.prompt("Rank : %d doing Bolo : %s and segment : %d" % (rank, bolo_name, segment))
if config.take_diff_signal:
signal, v, pol_ang = acquire_difference_signal(bolo_a, bolo_b, segment)
else:
signal, v, pol_ang = acquire_signal(bolo, segment)
if config.subtract_template:
signal_TEMPLATE = bolo_TEMPLATE.read_timestream(segment, read_list=["signal"])["signal"]
signal -= estimated_y*signal_TEMPLATE
print "Rank :", rank, "Bolos signal read"
hitpix = hp.vec2pix(config.nside_out, v[...,0], v[...,1], v[...,2])
del v
cov_ut.get_inv_cov_matrix(hitpix, pol_ang, signal, inv_cov_matrix_local, b_matrix_local, hitmap_local, npix, config.pol_type)
print "Rank :", rank, "Inverse covariance matrix generated"
if config.subtract_template:
del signal_TEMPLATE
del signal
del pol_ang
del hitpix
inv_cov_matrix_local_segment = distribute_matrix(inv_cov_matrix_local, "cov_matrix")
del inv_cov_matrix_local
b_matrix_local_segment = distribute_matrix(b_matrix_local, "b_matrix")
del b_matrix_local
hitmap_local_segment = distribute_matrix(hitmap_local, "hitmap")
del hitmap_local
cov_matrix_local_segment = cov_ut.get_covariance_matrix(inv_cov_matrix_local_segment, hitmap_local_segment, config.pol_type)
sky_map_local_segment = cov_ut.get_sky_map(cov_matrix_local_segment, b_matrix_local_segment, hitmap_local_segment, config.pol_type)
write_segments(hitmap_local_segment, "hitmap", recon_dir)
write_segments(inv_cov_matrix_local_segment, "inverse_covariance_matrix", recon_dir)
write_segments(cov_matrix_local_segment, "covariance_matrix", recon_dir)
write_segments(sky_map_local_segment, "sky_map", recon_dir)
def run_mpi():
if rank == 0:
make_data_dirs()
bolo_segment_dict = get_local_bolo_segment_list(rank, size, config.bolo_list, config.segment_list)
comm.Barrier()
time.sleep(0.1*rank)
print "Rank :", rank, "Local bolo segment list :\n", bolo_segment_dict
comm.Barrier()
tot_seg = 0
for keys in bolo_segment_dict.keys():
tot_seg += len(bolo_segment_dict[keys])
if "hitmap" in config.timestream_data_products:
hitmap_local = np.zeros(hp.nside2npix(config.nside_in))
for bolo_name in bolo_segment_dict.keys():
bolo = Bolo(bolo_name, config)
for segment in bolo_segment_dict[bolo_name]:
start_seg = time.time()
prompter.prompt("Doing Bolo : %s Segment : %d Rank : %d" % (bolo_name, segment+1, rank))
if "hitmap" in config.timestream_data_products:
hitmap_local += bolo.simulate_timestream(segment)
else:
bolo.simulate_timestream(segment)
stop_seg = time.time()
prompter.prompt("Rank : " + str(rank) + " Time taken : " + str(stop_seg - start_seg) + ". Projected time : " + str((stop_seg - start_seg)*tot_seg))
prompter.prompt("Done simulating")
if "hitmap" in config.timestream_data_products:
hitmap = np.zeros(hitmap_local.size)
comm.Reduce(hitmap_local, hitmap, MPI.SUM, 0)
scan_dir = os.path.join(config.general_data_dir, config.sim_tag, config.scan_tag)
if rank == 0:
hp.write_map(os.path.join(scan_dir, "hitmap_in.fits"), hitmap)
